Method and medium for customer product recommendations

ABSTRACT

Examples described herein relate to a system consistent with the disclosure. For instance, the system may comprise a data lake including information relating to an in-store activity of a customer and an online activity of the customer, a processing resource, and a non-transitory machine-readable medium storing instructions executable by the processing resource to identify the in-store activity and the online activity of the customer, aggregate and store the in-store activity and the online activity of the customer in the data lake, reduce an amount of products in a product portfolio, compare purchases including in-store purchases and online purchases, and recommend a product of the plurality of products based on the comparison.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a Divisional application and claims priority to U.S.patent application Ser. No. 16/220,345, which was filed on Dec. 14, 2018and is now U.S. patent Ser. No. 11/538,084, which is herein included byreference in its entirety for all purposes.

BACKGROUND

In terms of product sales in business-to-business (B2B) ecommerce,product providers may use a recommendation engine to generate andrecommend products based on an input. For example, the recommendationengine may identify products with behavior attributes of a customer.Recommendation engines may receive a vast amount of input before makinga recommendation.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of a system including a data lakeconsistent with the disclosure.

FIG. 2 illustrates an example of a system consistent with thedisclosure.

FIG. 3 illustrates an example of an apparatus suitable with a systemconsistent with the disclosure.

FIG. 4 illustrates an example of a method consistent with thedisclosure.

FIG. 5 illustrates an example diagram of a non-transitory machinereadable medium suitable with a system consistent with the disclosure.

FIG. 6 illustrates an example of charts consistent with the disclosure.

FIG. 7 illustrates an example of a system consistent with thedisclosure.

DETAILED DESCRIPTION

Systems may provide product recommendations to a customer based onpurchases and/or interest of the customer. Product recommendations maybe provided to the customer as the customer purchases products. Thesystem may make recommendations with input from a company productportfolio. As used herein, “product portfolio” refers to a catalog ofproducts offered for sale by a company.

However, the product portfolio may include a vast amount of products. Aproduct portfolio including a vast amount of products may cause therecommendation of redundant products. For example, the same and/orsimilar product may be recommended to the customer multiple times or atthe same time. Further, the including a vast amount of products maycause missed opportunities. That is, due to the amount of products inthe product database the system may not be able to recommend productsthat are useful and/or helpful to the customer. Including a vast productportfolio may prevent the system from creating a personalized userexperience for the customer.

Accordingly, customer product recommendations, as described herein, maycause a processing resource to identify in-store activity and the onlineactivity of a customer, aggregate the activity of the customer, andreduce the amount of products in the product portfolio by comparingproduct activity of customers. That is, products may be recommended to acustomer based on the product activity of the customer and othercustomers, as detailed herein.

In some examples, the system may utilize a data lake to aggregate thedata of a customer and analyzes the purchases and activities of thecustomer. As used herein, “customer” refers to a person and/or entitywith a data lake. The data from the data lake may then be used todetermine which products are purchased together and/or areinterdependent to each other. This may allow the system to predict whichrelated and/or additional products a customer is likely to purchasebased on the product activity of the customer. As used herein, “productactivity” refers to the act of a customer viewing, saving, liking,showing interest in, and/or purchasing a product.

Further, the system may recommend the related products, based on theproduct activity of a customer. Recommending relevant products to thecustomer may lead to a sale of an upgraded product and/or the sale ofrelated products. The recommendations are made based on the similaritiesbetween the related product and the interest products in the data lake,similarities between the products purchased by other customers, and theintent of the customer and other customers. Recommending products basedon the aggregation of customer data may provide the customer withspecific products that are tailored to their goals.

FIG. 1 illustrates an example of a system 100 including a data lake 102consistent with the disclosure. The data lake 102 may includeinformation relating to in-store activity of a customer and onlineactivity of a customer. That is, the system 100 may store the productactivity of a customer in the data lake 102. The system 100 may be ableto monitor and store in the data lake 102 which interest products thecustomer installs, when the customer calls the call center or customersupport and/or which interest products the customer discuss when callingthe call center or customer support, specific transactions performed bythe customer, the product category the customer purchases from, etc. Asused herein, “interest products” refers to a product or productsassociated with the data lake of a customer and/or a product or productsviewed, saved, liked, and/or purchased by a customer.

In addition, the system 100 may identify the product interactions of acustomer and store the data in the data lake 102. For example, if acustomer shows interest in a product on a third-party website, a productmenu, or in a physical store, the system 100 may identify the customerand store the interactions and/or activity of the customer in the datalake 102. For instance, the system 100 may be able to identify acustomer based on the internet protocol (IP) address of a customer andstore the interest products viewed by the customer in the data lake 102.The system 100 may be able to monitor the products the customer isinterested in and store the data in the data lake 102. For example, thesystem 100 may determine that the customer purchases products under acertain price point and store the information in the data lake 102. Thesystem 100 may then recommend products within the price point of thecustomer. As used herein, “data lake” refers to a centralized place thatcontains data of a customer's interest and product interactions bothin-store and online customer.

In some examples, the data lake 102 may store products purchased from anonline store. Similarly, the data lake 102 may store products purchasedin-store. As used herein, “in-store” refers to a physical store and/or astore that is not accessed by a computing device. As used herein,“online store” refers to a store that is accessed by a computing device.In some examples, the optimization and predictive analytics layer 116may utilize the information from the data lake 102 to produce anoptimized product portfolio 118. The optimized product portfolio 118 mayinclude a plurality of interdependent products. The analytics layer 116may use the data lake 102 of each customer to analyze product interestof customers and shape the demand of the product portfolio. That is, theanalytics layer 116 may use the data lake 102 of each customer todetermine which products a customer is most likely to purchase. As such,determining which products a customer is most likely to purchase mayallow the analytics layer 116 to limit the products offered in theproduct portfolio while maintaining the revenue produced from theproducts offered.

In some examples, limiting the products offered may reduce the productportfolio from a range of about 100,000 products offered to about 20,000products offered, including all individual values and subranges between.Reducing the product portfolio may produce an optimized productportfolio 118 and save resource by reducing the amount of maintenanceused to maintain the product portfolio. Further, reducing the productportfolio may cause the product menu to load the products faster, ascompared to a product portfolio with 100,000 products offered. In someexamples, limiting the products offered in the product portfolio mayallow the offering of efficient products that are useful to the customerat a better and/or cheaper cost, as compared to the cost of a productwhen 50,000 or more products are offered. Further, limiting the productsoffered to a customer may limit the number of rule sets (e.g., possiblerecommendations). That is, a decreasing the number of products offeredmay decrease the amount of possible recommendations to offer a customerand ensure that the customer receives useful recommendation toaccomplish the goals of the customer.

In some examples, the optimization and predictive analytics layer 116may utilize the data from the data lake 102 to provide productrecommendations 128 to a customer. As described in further detailsbelow, providing recommendation to a customer may increase the sale ofrelated products and/or assist the customer in carrying out a specifictask and/or goal. In some examples, the implementation layer 138 mayrecommend the products identified in the analytics layer 116 atdifferent locations. For example, as described below, the implementationlayer 138 may provide a customer with recommendations of related producton an eCommerce portal 148. For instance, recommendations may be made toa customer on a checkout menu, a product menu, and/or other pages on aneCommerce portal 148. In addition, recommendations may be made when acustomer calls a call center 162, through an email campaign 160 sent tothe customer, and/or through an offline campaign 164 (e.g., in a storeas a customer checks out).

FIG. 2 illustrates an example of a system 200 consistent with thedisclosure. System 200 is analogous or similar to system 100 of FIG. 1 .Data lake 202 is analogous or similar to data lake 102 of FIG. 1 . Insome examples, the system 200 may include a non-transitorymachine-readable medium 240 storing instructions executable by aprocessing resource 221. The non-transitory machine-readable medium 240may cause the processing resource 221 to rank related products 206 torecommend to a customer. For example, the data lake 202 may provide theanalytics layer (e.g., optimization and predictive analytics layer 116of FIG. 1 ) with products that the customer is interested in. Theoptimization and predictive analytics layer 116 may then use the dataprovided by the data lake 202 to rank related products 203 forrecommendation. As used herein, “related products” refers to a productor products that is/are interdependent to a product stored in the datalake and/or a product or products that is/are deemed useful to acustomer.

In some examples, the optimization and predictive analytics layer 116may use revenue coverage optimization to rank the products. The revenuecoverage optimization based ranking is based on at least two factors.The first factor the revenue coverage optimization based ranking isbased on is a product's individual contribution to revenue, volume,and/or margin. The second factor the revenue coverage optimization basedranking is based on is a product's ability to enable the sale of otherproducts in the product portfolio. Using the revenue coverageoptimization to rank products may maximize the cumulative revenue ofcustomer orders.

In some examples, related products 206 may be ranked to determine theproducts that are most useful to the customer. For example, thecharacteristics and/or intended purpose of the interest products 204 thecustomer is interested in is taken into account when ranking relatedproducts 206. The top ranking related products 206 may then berecommended to the customer by the implementation layer (e.g.,implementation layer 138 of FIG. 1 ). In addition, the optimization andpredictive analytics layer may determine how the interest product 204enables the sale of other related products 206. That is, thenon-transitory machine-readable medium 240 may cause the processingresource 221 to determine related products 206 that may be used inconjunction with the products 204.

Determining related products 206 that may be used with the interestproducts 204 may increase the sale of related products 206. In addition,determining related products 206 that may be used with the interestproducts 204 may assist the customer in carrying out a specific taskand/or goal. For instance, storing interest products 204 from both anonline store and a physical store in a data lake 202 may allow theanalytics layer to better understand the specific task and/or goal ofthe customer. For instance, non-transitory machine-readable medium 240may cause the processing resource to recommend related products 206 thatare specific to the goals of the customer based on interest products 204associated with the data lake 202 of a customer from an online store anda physical store.

In some examples, the system 200 may analyze the interdependence of aninterest product 204 in relation to related products 206. For example,if a customer purchases an interest product 204, in an online store or aphysical store, the non-transitory machine-readable medium 240 may causethe processing resource 221 to determine related products 206 that maybe used in conjunction with the interest product 204. As used herein,“interdependence” refers to two or more products depending on each otherto accomplish a task and/or two or more products that may be used inconjunction with each other. The system 200 may then recommend therelated products 206 to the customer.

In some examples, the system 200 may rank related products 206 based onhow often a related product 206 is purchased with the interest product204. For examples, the system 200 may compare the interest products 204of a customer, bought and/or viewed in a physical store and in an onlinestore, with similar purchases of other customers, bought in a physicalstore and in an online store. The system 200 may then determine based onthe purchases of the other customers, related products 206 to theinterest product 204 of a customer. The related products 206 may beranked based on the usefulness to the interest product 204 of thecustomer and then the system 200 may recommend the top related products206 to the customer. That is, the system 200 may determine interestproducts 204 of the customer that are similar to products purchased byother customers and then recommend related products 206 purchased by theother customers to the customer. The system 200 may recommend relatedproducts 206 to the customer based on the compared purchases of othercustomers and the ranking of related products 206.

As used herein, “similar purchase” refers to the purchase of asubstantially similar product by a customer compared to anothercustomer. As used herein, the term substantially intends that thecharacteristic does not have to be absolute but is close enough so as toachieve the characteristic. That is, “substantially similar” is notlimited to absolutely similar. For example, substantially similarproducts may include products that are intended to provide the samefunction. For instance, substantially similar products may be a routerfrom a first brand purchased by the customer and a router from a secondbrand purchased by another customer, a 3.5 GHz central processing unitpurchased by a customer and a 4.7 GHz central processing unit purchasedby another customer, etc. In some examples, substantially similarproducts may be the same exact product.

FIG. 3 illustrates an example of an apparatus 320 suitable with a systemconsistent with the disclosure. As illustrated in FIG. 3 , the apparatus320 includes a processing resource 321 and a memory resource 322. Theprocessing resource 321 may be a hardware processing unit such as amicroprocessor, application specific instruction set processor,coprocessor, network processor, or similar hardware circuitry that maycause machine-readable instructions to be executed. In some examples,the processing resource 321 may be a plurality of hardware processingunits that may cause machine-readable instructions to be executed. Theprocessing resource 321 may include central processing units (CPUs)among other types of processing units. The processing resource 321 mayalso include dedicated circuits and/or state machines, such as in anApplication Specific Integrated Circuit (ASIC), Field Programmable GateArray (FPGA) or similar design-specific hardware. The memory resource322 may be any type of volatile or non-volatile memory or storage, suchas random-access memory (RAM), flash memory, read-only memory (ROM),storage volumes, a hard disk, or a combination thereof.

The memory resource 322 may store instructions thereon, such asinstructions 323, 324, 325, 326 and 327. When executed by the processingresource 321, the instructions may cause the apparatus 320 to performspecific tasks and/or functions. For example, the memory resource 322may store instructions 323 which may be executed by the processingresource 321 to cause the apparatus 320 to identify a product activityof a first customer. In some examples, the system may identify theproduct interactions of a customer and store the interactions in thedata lake.

The memory resource 322 may store instructions 324 which may be executedby the processing resource 321 to cause the apparatus 320 to rankproducts related to the product activity of the first customer based onrevenue coverage optimization. In some examples, a data lake may storean interest product of a first customer. The apparatus 320 may thenreview the interest products stored in the data lake and create a listof products that are related to the interest products stored in the datalake. In some examples, after the apparatus 320 may generate a list ofrelated products. The list may then be ranked based on which relatedproducts are interdependent with the interest products in the data lake,which related products will assist the first customer in completing atask when combined with the interest products stored in the data lake,which related product is a first customer most likely to purchase, or acombination thereof. In some examples, the list may be ranked based onthe product's ability to enable the sale of other products in theproduct portfolio and the product's individual contribution to revenue,volume, and/or margin.

The top ranked products may then be recommended to the first customer.For instance, the apparatus 320 may recommend the top ranked relatedproducts to the first customer in the checkout menu, in the productmenu, through an email. That is, the apparatus may use features (e.g.,eCommerce portal 148, call center 162, email campaign 160, and/oroffline campaign 164 of FIG. 1 ) of the implementation layer(implementation layer 138 of FIG. 1 ) to recommend related products tothe first customer.

The memory resource 322 may store instructions 325 which may be executedby the processing resource 321 to cause the apparatus 320 to compareproducts associated with the product activity of the first customer toproducts purchased by a second customer. In some examples, a secondcustomer may purchase a product to accomplish a particular goal and/ortask. The second customer may also purchase additional products relatedto the product. The additional/related products may assist the secondcustomer in accomplishing the goal and/or task. If a first customerpurchases a substantially similar product as the second customer, theapparatus 320 may compare the two products and conclude that the firstcustomer may have a substantially similar goal and/or task as the secondcustomer. The apparatus 320 may then recommend the sameadditional/related products relating to the product purchased by thesecond customer to the first customer. Recommending the sameadditional/related products may assist the first customer inaccomplishing their goal and/or task. That is, the apparatus 320 maycompare the goal and/or task of the first customer and a second customerto determine which related products should be recommended to the firstcustomer.

In some examples, the apparatus 320 may compare products purchased inthe same quarter time period or in different time periods. For examples,the apparatus 320 may compare a product purchased by the second customerin a first quarter of a year with a product stored in the data lake ofthe first customer in a second quarter of a year.

The memory resource 322 may store instructions 326 which may be executedby the processing resource 321 to cause the apparatus 320 to recommendrelated at least one product to the first customer from a productportfolio offering based on the comparison of products, affinity ofproducts, similarity of customers purchasing the products, and digitalcustomer intent, wherein the product portfolio offering comprises aplurality of products having interdependencies. In some examples, theproducts purchased by a second customer may inform the apparatus 320which related products to recommend to a first customer. For example, ifthe first customer and the second customer are determined to be asubstantially similar customer type and are purchasing products toaccomplish the substantially similar goal and/or task, the productsviewed and/or purchased by the second customer may assist inaccomplishing the goal and/or task of the first customer. As such, theapparatus 320 may recommend related products purchased by a secondcustomer and not the first customer. Recommending related productspurchased by a second customer and not the first customer may assist thefirst customer with accomplishing their goal and/or task and mayincrease the sale of products.

The memory resource 322 may store instructions 327 which may be executedby the processing resource 321 to cause the apparatus 320 to aggregateand store data of the first customer into a data lake based on theproduct activity of the first customer and global purchases of the firstcustomer. In some examples, a data lake may contain all informationrelated to purchases made by first customer. As described below, thedata lake may store information on product activity made in a localbranch and/or a company headquarter. That is, the data lake may storeall product activity made by a first customer to provide useful andup-to-date product recommendations to the first customer. Aggregatingall product activity in one location may allow the apparatus 320 to makecurrent recommendations based on the first customer's goals.

The data lake may also store data about products the first customer isinterested in. For example, if the first customer likes a product thedata lake may store the data and the apparatus 320 may use the data tomake related product recommendations. For instance, if a first customerviews a product multiple times but does not purchase the product theapparatus 320 may determine that the first customer likes the product oris interested in the product. The apparatus 320 may then use the data tomake related product recommendations. If the first customer saves aproduct to purchase later the data lake may store the data and theapparatus 320 may also use the data to make related productrecommendations.

FIG. 4 illustrates an example of a method 430 consistent with thedisclosure. Method 430 may be performed, for example, by a processingresource (e.g., processing resource 221 of FIG. 2 ) of a system (e.g.,system 200 of FIG. 2 ). In some examples, the method 430 may beperformed with more or less elements.

At 431, the method 430 may include identifying the in-store activity andthe online activity of a first customer. In some examples, the in-storeactivity and online activity of the first customer may be stored in thedata lake. In addition, the in-store activity and online activity of thefirst customer may be compared to other products to make recommendationto the first customer.

At 432, the method 430 may include ranking products based on revenuecoverage optimization (RCO), the ability of the product to complete atask of the first customer, or a combination thereof. In some examples,the system may determine the similarities between products to determinethe product affinity between the products. As used herein, “productaffinity” refers to the similarity of the characteristics and functionof a product with another product. In some examples, the system may usethe similarities between the products stored in the data lake of thefirst customer and the products purchased by a second customer todetermine related products to recommend to the first customer. Thesystem may then rank the related products to determine which products torecommend to the first customer.

In some examples, the system may determine which customers aresubstantially similar. That is, the system may determine if a firstcustomer and a second customer are categorized as a substantiallysimilar customer type. As used herein, “customer type” refers tosimilarities between the interests and/or business attributes of acustomer with another customer. For example, customers may be asubstantially similar customer type if the customers share similarattributes. For instance, customers may share similar attributes if thecustomers have over 10,000 employees, if the customers are in the sameindustry (e.g., oil & gas, etc.), etc.

In some examples, customers that are categorized as substantiallysimilar customer type may purchase substantially similar products. Assuch, the system may recommend the products purchased by the secondcustomer to the first customer when it is determined that the firstcustomer and the second customer have substantially similar customerintent. In some examples, the system may rank the related productsbefore recommending the related products to the first customer. As usedherein, “customer intent” refers to similarities between the goal and/orpurpose of a customer with the goal and/or purpose of another customer.In some examples, the customer intent may be based on online activitywhich may show the digital customer intent. In some examples, thecustomer intent may be based on in-store activity or a combination ofboth online and in-store activity.

In some examples, the system may determine which related products willassist the first customer in completing their goal based on the goals ofa second customer with a substantially similar intent. The system maydetermine if there is a substantial similar customer intent based onproducts purchased by the second customer and interest products of thefirst customer. The related products may then be ranked and the mostuseful related products may be recommended to the first customer.

In some examples, the related products may be ranked based on theability of a related product to assist the first customer with theirgoal and the ability of the related product to sale other products. Thatis, the interest product of the first customer may assist in the sale ofrelated products because the related products may assist inaccomplishing the goals of the first customer Ranking related productson the ability to sale other products may increase the sale of productsin physical stores and in online stores. For instance, when a relatedproduct is recommended to the first customer because it may help thefirst customer complete a task, the first customer is likely to purchasethe product.

At 433, the method 430 may include determining products to recommendbased on substantially similarity between products associated with thein-store and online activity of the first customer and productspurchased by a second customer, an intended use between the interestproducts associated with the in-store and online activity of the firstcustomer and the intended use of the product purchased by the secondcustomer, or a combination thereof. In some examples, if a firstcustomer and a second customer purchase a substantially similar product,other related products purchased by the second customer may berecommended to the first customer.

The method 430 may compare the products in the customer cart of thefirst customer with the products purchased by the second customer inorder to recommend related products to the first customer. That is, themethod 430 may determine related products to recommend based thecompared products in the customer cart of the first customer and theproducts purchased by the second customer.

At 434, the method 430 may include recommending products from theproduct portfolio offering to the first customer based on the productsassociated with the in-store and online activity of the first customer.In some examples, the products purchased in a physical store and anonline store by a first customer may be compared to products purchasedby other customers. The comparison may allow the processing resource torecommend products purchased by the other customers to the firstcustomer.

At 435, the method 430 may include aggregating data of the firstcustomer based on the in-store and online activity of the firstcustomer. In some examples, aggregating all account activity to onelocation may allow the processing resource to make recommendations basedon the first customer's goals. As such, a data lake may contain allinformation related to purchases and/or activity of a first customer.For example, the data lake may store data about purchases/activity in aphysical store, purchases/activity made in an online store,purchases/activities made in a local market and/or a global market, etc.

In some examples, the purchases and activity of a first customer mayprovide information on the technological demands of a first customer. Assuch, aggregating the digital footprint at three levels may assist inmaking recommendations to the first customer based on theirtechnological demands. That is, aggregating the digital footprint of thefirst customer may include aggregating product activity associated withthe first customer's user ID or email address, aggregating productactivity associated with the first customer's organization ID or countryaccount ID, and aggregating product activity associated with the firstcustomer's root organization ID or global account ID. As used herein,“digital footprint” refers to product activity of a customer, includingboth in-store activity and online activity.

In some examples, the data lake of a first customer may store the onlineactivity and in-store activity (e.g., purchases) made by a user ID oremail address and carried out by a company contact person (e.g., anemployee). In addition, the data lake of a first customer may store theonline activity and in-store activity made by an organization ID orcountry account ID and carried out by a company branch. Further, thedata lake of a first customer may store the online activity and in-storeactivity made by a root organization ID or global account ID and carriedout by a company headquarter (e.g., corporation headquarter). In someexamples, the system may utilize a first customer's activity from a userID, organizational ID, and/or root organizational ID to recommendrelated products to the customer. That is, the system may be able toaggregate activity at different levels to determine recommendations forthe first customer.

FIG. 5 illustrates an example diagram of a non-transitory machinereadable medium 540 suitable with a system consistent with thedisclosure. The non-transitory machine-readable medium 540 may be anytype of volatile or non-volatile memory or storage, such asrandom-access memory (RAM), flash memory, read-only memory (ROM),storage volumes, a hard disk, or a combination thereof.

The medium 540 stores instructions 541 executable by a processingresource to rank products related to the product activity of the firstcustomer based on revenue coverage optimization (RCO). In some examples,the medium 540 may determine which products are related to productsstored in the data lake of a first customer. The medium 540 may thenrank the related products to determine which related products torecommend to the first customer. The medium 540 may rank the relatedproducts based on which product the first customer is most likely tobuy, which product would allow the first customer to accomplish theirgoal and/or task, amongst other possibilities. In some examples, themedium 540 may recommend the top five related products of the rankedproducts to the first customer. However, this disclosure is not solimited. For example, the medium 540 may recommend the top two relatedproducts or as many related products that may assist the first customerin accomplishing their goal.

The medium 540 stores instructions 542 executable by a processingresource to identify a product activity of a first customer. The medium540 stores instructions 543 executable by a processing resource tocompare products associated with the product activity of the firstcustomer to products purchased by a second customer. In some examples,the medium 540 may recommend products purchased by a second customer toa first customer. That is, the medium may compare interest products of afirst customer to products purchased and/or viewed by a second customerto make recommendations to the first customer. In some examples,comparing and then recommending products may assist the first customerin completing a specific task and may also increase the sale ofproducts.

The medium 540 stores instructions 544 executable by a processingresource to recommend products to the first customer from a productportfolio offering based on the comparison of products, affinity ofproducts, similarity of customers purchasing the products, and digitalcustomer intent. In some examples, the medium 540 may recommend productsto the first customer after comparing substantially similar productspurchased by multiple customers. For example, if multiple customerspurchased the same additional products after purchasing a particularproduct the medium 540 may determine that the additional products arerelated to the purchased product and may recommend the additionalproducts to the first customer. That is, the medium 540 may determinethat the purchased product and the additional products areinterdependent. In some examples, comparing substantially similarproducts purchased by multiple customers may determine if the customershave substantially similar goals and/or tasks.

The medium 540 stores instructions 545 executable by a processingresource to aggregate and store data of the first customer into a datalake based on the product activity of the first customer and globalpurchases of the first customer. A data lake may contain all informationrelated to products a first customer is interested in. For example, thedata lake may store all product activity made by a first customer. Forinstance, the data lake may store each time a first customer views aproduct, saves a product, purchases a product in a physical store,purchases a product in an online store, etc. In addition, the data lakemay store activity made locally at a company branch and/or globally atthe company headquarters. Aggregating all activity of a first customerin one location may allow the medium 540 to make recommendations basedon the first customer's goals.

The medium 540 stores instructions 546 executable by a processingresource to recommend the ranked products related to the first customerbased on the product activity of the second customer. In some examples,the medium 540 may rank product related to the first customers earlierpurchases. For example, the first customer may have purchased a productone year ago, for example, and the medium 540 may recommend asubstantially similar product and/or an updated version. The medium 540may then rank the products to determine which product the first customeris most likely to buy and then recommend the product to the firstcustomer.

The medium 540 stores instructions 547 executable by a processingresource to recommend related products to the first customer based ononline purchases of the second customer and in-store purchases of thesecond customer. In some examples, if the first customer and the secondcustomer purchase substantially similar products the additional productspurchased by the second customer may be useful to the first customer.The medium 540 may then recommend the additional products purchased by asecond customer and not the first customer to the first customer.

The medium 540 stores instructions 548 executable by a processingresource to analyze an interdependence of products purchased by thesecond customer. In some examples, the medium 540 may determine if aproduct and additional products purchased by the second customer areinterdependent. The medium 540 may analyze how often the additionalproducts were purchased with the product when other customers purchasedthe product. If it is determined that the product and additionalproducts are interdependent then the medium 540 may recommend theadditional products to the first customer. In some examples, analyzingthe interdependence between products may allow the for the productportfolio to be reduced. For example, if it is determined that a productis not useful and/or interdependent the system may determine that theproduct is to be removed from the product portfolio.

FIG. 6 illustrates an example of charts 650-1 and 650-2 consistent withthe disclosure. Chart 650-1 represents customer likes and dislikes ofproducts without a prediction. Chart 650-2 represents customer likes anddislikes of products with a prediction. In some examples, a system(e.g., system 200 of FIG. 2 ) may cause the processing resource (e.g.,processing resource 221 of FIG. 2 ) to recommend products to a firstcustomer 651 based on similarities with other customers (e.g., secondcustomer 652, third customer 653, and/or fourth customer 654). Firstcustomer 651, second customer 652, third customer 653, and fourthcustomer 654 collectively refer to first customer 651-1, second customer652-1, third customer 653-1, and fourth customer 654-1 of chart 650-1and first customer 651-2, second customer 652-2, third customer 653-2,and fourth customer 654-2 of chart 650-2, respectively. That is, thesystem may predict which products a first customer 651 will like basedon the similarities between the first customer 651 and other customers.For instance, the system may determine if the first customer 651 is asubstantially similar customer type as the other customers.

In some examples, the system may compare the likes and dislikes of afirst customer 651 to the likes and dislikes of other customers. Thatis, the system may determine if a first customer likes a product (e.g.,fourth product 658-1) which is currently not part of the data lake ofthe first customer 651-1 but have been purchased by other customers thathave similar interest. That is, the system may determine if customers(e.g., first customer 651, fourth customer 654) are similar based on thepurchasing and/or interest of substantially similar products by thecustomers. If the number of products the customers like and dislikeexceeds a threshold it may be determined that the customers are similar.If it is determined that the customers are similar, the system mayrecommend products purchased by one customer and not the other. Incontrast, if it is determined that the customers are not similar thesystem may not recommend products purchased by one customer and not theother.

For example, the system may compare the product likes and dislikes ofthe first customer 651 and the fourth customer 654 and determine if twocustomers are similar. Here, based on the data lakes of the firstcustomer 651 and the fourth customer 654 it is known that both the firstcustomer 651 and the fourth customer 654 like the first product 655 andthe second product 656. However, it is not known if the fourth customer654 dislikes the third product 657 as the first customer 651 does. Inaddition, it is not known if the first customer 651 likes the fourthproduct 658 as the fourth customer 654 does. Therefore, the system maydetermine that there are not enough similarities between the firstcustomer 651 and the fourth customer 654 to make a proper predictionand/or determination of the first customer's likes and dislikes. Firstproduct 655, second product 656, third product 657, and fourth product658 collectively refer to first product 655-1, second product 656-1,third product 657-1, and fourth product 658-1 of chart 650-1 and firstproduct 655-2, second product 656-2, third product 657-2, and fourthproduct 658-2 of chart 650-2, respectively.

In addition, the system may compare the product likes and dislikes ofthe first customer 651 and the third customer 653 to determine if thetwo customers are similar. Here, based on the data lakes of the firstcustomer 651 and the third customer 653 do not have similar likes anddislike in regard to the second product 656 and the third product 657.Further, it is not known if the third customer 653 likes the firstproduct 655 as the first customer 651 does. In addition, it is not knownif either the first customer 651 or the third customer 653 likes ordislikes the fourth product 658. Therefore, the system may determinethat the first customer 651 and the third customer 653 are not similar.

Likewise, the system may compare the product likes and dislikes of thefirst customer 651 and the second customer 652 to determine if the twocustomers are similar. Here, based on the data lakes of the firstcustomer 651 and the second customer 652 it is known that the firstcustomer 651 and the second customer 652 both like the first product 655and the second product 656 and both dislike the third product 657.However, it is unknown if the first customer 651 likes the fourthproduct 658 as the second customer 652 does. Therefore, the system maydetermine that the first customer 651 and the second customer 652 aresimilar since both customers like and dislike the same products (e.g.,the first product 655, second product 656, and third product 657) andthe number of products that the first customer 651 and the secondcustomer 652 like and/or dislike exceeds a threshold. As such, thesystem may predict (represented by the underlined check mark) that thefirst customer 652-2 likes the fourth product 658-2. In addition, thesystem may determine that the first customer 651 would likely purchasethe fourth product 658. Based on the prediction the system may recommendthe fourth product 658 to the first customer 651.

FIG. 7 illustrates an example of a system 700 consistent with thedisclosure. In some examples, the system 700 may include a data lake702. Data lake 702 is analogous or similar to data lake 102 and 202 ofFIGS. 1 and 2 , respectively. Product 704 is analogous or similar toproduct 204 of FIG. 2 . Related product 706 is analogous or similar torelated product 206 of FIG. 2 . System 700 is analogous or similar tosystem 100 and 200 of FIGS. 1 and 2 , respectively.

In some examples, the system 700 may also include a non-transitorymachine-readable medium (e.g., non-transitory machine-readable medium240 of FIG. 2 ) storing instructions executable by a processing resource(e.g., processing resource 221 of FIG. 2 ). The non-transitorymachine-readable medium may cause the processing resource to predictwhich related products 706 a customer is likely to purchase basedsimilar likes and dislikes of other customers. In some examples, if itis determined that a customer would like a related product, theprocessing resource may recommend the related product to the customer.That is, the system 700 may determine which products the customer islikely to purchase based on the product activity of the customer storedin the data lake 702 and the product activity of other customers.

In some examples, the system 700 may determine which products arepurchased together and determine if the customer has purchased and/orviewed one of the products that are purchased together. In someexamples, the system 700 may determine which related products 706 areuseful to the customer and recommend the related product 706 to thecustomer based on the interest product 704 of the customer.

In some examples, a customer may view interest products 704 on a productmenu and save the interest products 704 to a customer cart 708 topurchase at a later time. The interest products 704 saved in thecustomer cart 708 may be stored in the data lake 702 of the customer. Insome examples, the system 700 may rank products related to the interestproducts 704 in the customer cart 708 to determine which relatedproducts 706 to recommend to the customer. In some examples, the system700 may compare the interest products 704 to products purchased and/orliked by other customers to determine which related products 706 torecommend to the customer. That is, if a customer views and/or saves aninterest product 704 to the customer cart 708 the system 700 maydetermine related products 706 that may be used in conjunction with theviewed and/or saved products 704. The system 700 may then recommend therelated products 706 to the customer. As used herein, “customer cart”refers to a place in the online store where products are saved.

Products may be recommended to a customer in a variety of differentways. For example, the product recommendations may be made on a homepage of an online store, a checkout menu 710, a product menu 712, screen714, etc. In addition, the product recommendations may be made at anin-store register. For example, as a customer is checking out at theregister a computer screen 714 may recommend products to the customer.That is, the system 700 is able to target and make recommendations to acustomer in a physical store and an online store based on the data lake702.

The figures herein follow a numbering convention in which the firstdigit corresponds to the drawing figure number and the remaining digitsidentify an element or component in the drawing. Elements shown in thevarious figures herein may be capable of being added, exchanged, and/oreliminated so as to provide a number of additional examples of thedisclosure. In addition, the proportion and the relative scale of theelements provided in the figures are intended to illustrate the examplesof the disclosure and should not be taken in a limiting sense.

It should be understood that the descriptions of various examples maynot be drawn to scale and thus, the descriptions may have a differentsize and/or configuration other than as shown therein.

What is claimed:
 1. A system comprising: a data lake includinginformation relating to an in-store activity of a customer and an onlineactivity of the customer; a processing resource; and a non-transitorymachine-readable medium storing instructions executable by theprocessing resource to: identify the in-store activity and the onlineactivity of the customer; aggregate and store the in-store activity andthe online activity of the customer in the data lake; reduce a number ofproducts displayed in a product portfolio offering to the customer basedon revenue coverage optimization (RCO), wherein the product portfoliooffering comprises at least a plurality of products havinginterdependencies; and recommend at least one product from the productportfolio offering based on a combination of affinity of products,similarity of customers purchasing the products, and digital customerintent.
 2. The system of claim 1, including instructions to rank theplurality of products in the product portfolio offering based on anability of each product to link to a sale of another product.
 3. Thesystem of claim 1, further comprising instructions to determine whichproducts associated with the data lake of the customer are substantiallysimilar to the purchases of another customer.
 4. The system of claim 3,wherein the instructions to compare include instructions to compare anintended use of the products associated with the data lake of thecustomer with the intended use of the purchased products by the anothercustomer.
 5. The system of claim 3, wherein the instructions torecommend include instruction to recommend products purchased by theanother customer to the customer based on a product affinity between theproducts purchased by the another customer and the products associatedwith the data lake of the customer.
 6. The system of claim 1, whereinthe digital customer intent is based on the similarities between a taskof the customer and a task of the another customer.
 7. A non-transitorymachine-readable medium storing instructions executable by a processingresource to: identify a product activity of a first customer; compareproducts associated with the product activity of the first customer toproducts purchased by a second customer, rank products related to theproduct activity of the first customer based on revenue coverageoptimization (RCO); recommend at least one product to the first customerfrom a product portfolio offering based on the comparison of products,affinity of products, similarity of customers purchasing the products,and digital customer intent, wherein the product portfolio offeringcomprises a plurality of products having interdependencies; andaggregate and store data of the first customer into a data lake based onthe product activity of the first customer and global purchases of thefirst customer.
 8. The non-transitory machine-readable medium of claim7, further including instructions to reduce a number of productsdisplayed in a product portfolio offering.
 9. The non-transitorymachine-readable medium of claim 7, further including instructions torecommend the ranked products related to the first customer based on theproduct activity of the second customer.
 10. The non-transitorymachine-readable medium of claim 7, further including instructions torecommend related products to the first customer based on onlinepurchases of the second customer and in-store purchases of the secondcustomer.
 11. The non-transitory machine-readable medium of claim 7,further including instructions to analyze an interdependence of productspurchased by the second customer.
 12. The non-transitorymachine-readable medium of claim 11, further including instructions torecommend products purchased by the second customer determined to beinterdependent to products associated with the product activity of thefirst customer.
 13. The non-transitory machine-readable medium of claim7, wherein the products are ranked based on the ability of a product tocomplete a task of the first customer.
 14. The non-transitorymachine-readable medium of claim 7, wherein products are recommendedbased on similarities between the intended use of the productsassociated with the product activity of the first customer and theintended use of the products purchased by the second customer.
 15. Thenon-transitory machine-readable medium of claim 7, wherein the productsare recommended to the first customer on an online menu, at an in-storeregister, at an eCommerce portal, through a call center, through anemail campaign, on an offline campaign, or a combination thereof.
 16. Amethod comprising: identifying an in-store activity and an onlineactivity of a first customer; determining products to recommend based onsubstantially similarity between products associated with the in-storeand online activity of the first customer and products purchased by asecond customer, an intended use between the products associated withthe in-store and online activity of the first customer and the intendeduse of the product purchased by the second customer, or a combinationthereof; ranking products based on revenue coverage optimization (RCO),the ability of the product to complete a task of the first customer, ora combination thereof; recommending products from the product portfoliooffering to the first customer based on the products associated with thein-store and online activity of the first customer, affinity ofproducts, similarity of customers purchasing the products, digitalcustomer intent, or a combination thereof; and aggregating data of thefirst customer based on the in-store and online activity of the firstcustomer.
 17. The method of claim 16, further comprising comparing theproducts associated with the in-store and online activity of the firstcustomer with the products purchased by the second customer.
 18. Themethod of claim 17, further comprising recommending products based onthe compared products associated with the in-store and online activityof the first customer and the products purchased by the second customer.19. The method of claim 16, further comprising recommending the firstfive products of the ranked products.
 20. The method of claim 16,further comprising aggregating and storing data of the first customerinto a data lake.